
%%time
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
import pandas_profiling as prf
%matplotlib inline

Wall time: 5.99 ms


netflix = pd.read_csv(r"C:\Users\modem\Downloads\netflix_data.csv")


netflix


netflix_copy = netflix.copy()


netflix_copy.head()


netflix.shape

(8807, 13)


netflix.columns

Index(['Unnamed: 0', 'show_id', 'type', 'title', 'director', 'cast', 'country',
       'date_added', 'release_year', 'rating', 'duration', 'listed_in',
       'description'],
      dtype='object')


netflix.describe()


netflix.describe(include='all')


netflix.sort_values(by=['release_year'],ascending=False).head(5)


netflix.nunique()

Unnamed: 0      8807
show_id         8807
type               2
title           8804
director        4528
cast            7692
country          746
date_added      1767
release_year      74
rating            17
duration         220
listed_in        514
description     8775
dtype: int64


netflix.corr()


netflix.tail()


netflix.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8807 entries, 0 to 8806
Data columns (total 13 columns):
 #   Column        Non-Null Count  Dtype 
---  ------        --------------  ----- 
 0   Unnamed: 0    8807 non-null   int64 
 1   show_id       8807 non-null   object
 2   type          8807 non-null   object
 3   title         8807 non-null   object
 4   director      6173 non-null   object
 5   cast          7982 non-null   object
 6   country       7976 non-null   object
 7   date_added    8797 non-null   object
 8   release_year  8807 non-null   int64 
 9   rating        8803 non-null   object
 10  duration      8804 non-null   object
 11  listed_in     8807 non-null   object
 12  description   8807 non-null   object
dtypes: int64(2), object(11)
memory usage: 894.6+ KB


(netflix.isnull().sum()/len(netflix))*100

Unnamed: 0       0.000000
show_id          0.000000
type             0.000000
title            0.000000
director        29.908028
cast             9.367549
country          9.435676
date_added       0.113546
release_year     0.000000
rating           0.045418
duration         0.034064
listed_in        0.000000
description      0.000000
dtype: float64


profile_before = prf.ProfileReport(netflix)


profile_before

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profile_before.to_file(output_file="Netflix_Before_PreProcessing.html")

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netflix.head()


## Unnesting director column

dir_constraint=netflix['director'].apply(lambda x: str(x).split(', ')).tolist()
df1 = pd.DataFrame(dir_constraint, index = netflix['title']) 
df1 = df1.stack()
df1 = pd.DataFrame(df1.reset_index())
df1.rename(columns={0:'Directors'},inplace=True)
df1 = df1.drop(['level_1'],axis=1)
df1.head(10)


## Unnesting - cast column

cast_constraint=netflix['cast'].apply(lambda x: str(x).split(', ')).tolist()
df2 = pd.DataFrame(cast_constraint, index = netflix['title']) 
df2 = df2.stack()
df2 = pd.DataFrame(df2.reset_index())
df2.rename(columns={0:'Actors'},inplace=True)
df2 = df2.drop(['level_1'],axis=1)
df2.head(10)


netflix.head()


## Unnesting - listed_in column

listed_constraint=netflix['listed_in'].apply(lambda x: str(x).split(', ')).tolist()
df3 = pd.DataFrame(listed_constraint, index = netflix['title']) 
df3 = df3.stack()
df3 = pd.DataFrame(df3.reset_index())
df3.rename(columns={0:'Genre'},inplace=True)
df3 = df3.drop(['level_1'],axis=1)
df3.head(10)


## Unnesting - country column

country_constraint=netflix['country'].apply(lambda x: str(x).split(', ')).tolist()
df4 = pd.DataFrame(country_constraint, index = netflix['title']) 
df4 = df4.stack()
df4 = pd.DataFrame(df4.reset_index())
df4.rename(columns={0:'Country'},inplace=True)
df4 = df4.drop(['level_1'],axis=1)
df4.head(10)


df5 = df2.merge(df1,on=['title'],how='inner')

df6 = df5.merge(df3,on=['title'],how='inner')

df7 = df6.merge(df4,on=['title'],how='inner')


df7.head()

df7


df7.shape

(203163, 5)


netflix = df7.merge(netflix[['show_id', 'type', 'title', 'date_added',
       'release_year', 'rating', 'duration']],on=['title'],how='left')
netflix.head()


netflix.shape

(204539, 11)


netflix.isna().sum()

title             0
Actors            0
Directors         0
Genre             0
Country           0
show_id           0
type              0
date_added      158
release_year      0
rating           67
duration          3
dtype: int64


total_null = netflix.isnull().sum().sort_values(ascending = False)
percent = ((netflix.isnull().sum()/netflix.isnull().count())*100).sort_values(ascending = False)
print("Total records = ", netflix.shape[0])

missing_data = pd.concat([total_null,percent.round(2)],axis=1,keys=['Total Missing','In Percent'])
missing_data.head(10)

Total records =  204539


import numpy as np


## some columns having nan which is missing value, we have to replace

netflix['Actors'].replace(['nan'],['Unknown Actor'],inplace=True)
netflix['Directors'].replace(['nan'],['Unknown Director'],inplace=True)
netflix['Country'].replace(['nan'],[np.nan],inplace=True)
netflix.head()


total_null = netflix.isnull().sum().sort_values(ascending = False)
percent = ((netflix.isnull().sum()/netflix.isnull().count())*100).sort_values(ascending = False)
print("Total records = ", netflix.shape[0])

missing_data = pd.concat([total_null,percent.round(2)],axis=1,keys=['Total Missing','In Percent'])
missing_data.head(10)

Total records =  204539


netflix[netflix['duration'].isnull()]


netflix.loc[netflix['duration'].isnull(),'duration'] = netflix.loc[netflix['duration'].isnull(),'duration'].fillna(netflix['rating'])
netflix.loc[netflix['rating'].str.contains('min', na=False),'rating'] = 'NR'
netflix['rating'].fillna('NR',inplace=True)
netflix.isnull().sum()

title               0
Actors              0
Directors           0
Genre               0
Country         12497
show_id             0
type                0
date_added        158
release_year        0
rating              0
duration            0
dtype: int64


for i in netflix[netflix['date_added'].isnull()]['release_year'].unique():
    date = netflix[netflix['release_year'] == i]['date_added'].mode().values[0]
    netflix.loc[netflix['release_year'] == i,'date_added'] = netflix.loc[netflix['release_year']==i,'date_added'].fillna(date)


netflix.isnull().sum()

title               0
Actors              0
Directors           0
Genre               0
Country         12497
show_id             0
type                0
date_added          0
release_year        0
rating              0
duration            0
dtype: int64


for i in netflix[netflix['Country'].isnull()]['Directors'].unique():
    if i in netflix[~netflix['Country'].isnull()]['Directors'].unique():
        country = netflix[netflix['Directors'] == i]['Country'].mode().values[0]
        netflix.loc[netflix['Directors'] == i,'Country'] = netflix.loc[netflix['Directors'] == i,'Country'].fillna(country)


netflix.isnull().sum()

title              0
Actors             0
Directors          0
Genre              0
Country         4276
show_id            0
type               0
date_added         0
release_year       0
rating             0
duration           0
dtype: int64


for i in netflix[netflix['Country'].isnull()]['Actors'].unique():
    if i in netflix[~netflix['Country'].isnull()]['Actors'].unique():
        imp = netflix[netflix['Actors'] == i]['Country'].mode().values[0]
        netflix.loc[netflix['Actors'] == i,'Country'] = netflix.loc[netflix['Actors']==i,'Country'].fillna(imp)


netflix.isnull().sum()

title              0
Actors             0
Directors          0
Genre              0
Country         2069
show_id            0
type               0
date_added         0
release_year       0
rating             0
duration           0
dtype: int64


netflix['Country'].fillna('Unknown Country',inplace=True)       
netflix.isnull().sum()

title           0
Actors          0
Directors       0
Genre           0
Country         0
show_id         0
type            0
date_added      0
release_year    0
rating          0
duration        0
dtype: int64


netflix.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 204539 entries, 0 to 204538
Data columns (total 11 columns):
 #   Column        Non-Null Count   Dtype 
---  ------        --------------   ----- 
 0   title         204539 non-null  object
 1   Actors        204539 non-null  object
 2   Directors     204539 non-null  object
 3   Genre         204539 non-null  object
 4   Country       204539 non-null  object
 5   show_id       204539 non-null  object
 6   type          204539 non-null  object
 7   date_added    204539 non-null  object
 8   release_year  204539 non-null  int64 
 9   rating        204539 non-null  object
 10  duration      204539 non-null  object
dtypes: int64(1), object(10)
memory usage: 26.8+ MB


#converting date added data type(object format) into datetime format to extract years, month 

netflix["date_added"] = pd.to_datetime(netflix['date_added'])


netflix.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 204539 entries, 0 to 204538
Data columns (total 11 columns):
 #   Column        Non-Null Count   Dtype         
---  ------        --------------   -----         
 0   title         204539 non-null  object        
 1   Actors        204539 non-null  object        
 2   Directors     204539 non-null  object        
 3   Genre         204539 non-null  object        
 4   Country       204539 non-null  object        
 5   show_id       204539 non-null  object        
 6   type          204539 non-null  object        
 7   date_added    204539 non-null  datetime64[ns]
 8   release_year  204539 non-null  int64         
 9   rating        204539 non-null  object        
 10  duration      204539 non-null  object        
dtypes: datetime64[ns](1), int64(1), object(9)
memory usage: 26.8+ MB


## Removing the min string in duration column

netflix ['duration'] = netflix['duration'].str.replace(" min","")
netflix.head(6)


netflix['duration2'] = netflix.duration.copy()
netflix_ = netflix.copy()


netflix_.loc[netflix_['duration2'].str.contains('Season'),'duration2'] = 0
netflix_['duration2'] = netflix_.duration2.astype('int')
netflix_.head()


netflix_.duration2.describe()

count    204539.000000
mean         77.503151
std          52.443402
min           0.000000
25%           0.000000
50%          95.000000
75%         112.000000
max         312.000000
Name: duration2, dtype: float64


netflix_.T.apply(lambda x: x.nunique(), axis=1)

title            8804
Actors          36440
Directors        4994
Genre              42
Country           127
show_id          8807
type                2
date_added       1714
release_year       74
rating             14
duration          220
duration2         206
dtype: int64


profile_clean = prf.ProfileReport(netflix)
profile_clean

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profile_clean.to_file(output_file='Netflix_After_PreProcessing.html')

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##method1
netflix.groupby('type')['title'].count().sort_values(ascending=False)

type
Movie      147799
TV Show     56740
Name: title, dtype: int64


netflix['type'].value_counts().to_frame('values_count')


netflix.groupby(["type","release_year"])["title"].agg(pd.Series.mode)

type     release_year
Movie    1942                                         The Battle of Midway
         1943            [Undercover: How to Operate Behind Enemy Lines...
         1944                                             Tunisian Victory
         1945                                      Know Your Enemy - Japan
         1946                                           Let There Be Light
                                               ...                        
TV Show  2017                                                       Narcos
         2018                                                        9-Feb
         2019                                                  Creeped Out
         2020                                                     The Eddy
         2021                                                     Navarasa
Name: title, Length: 119, dtype: object


## Histogram to see the distribution of duration

plt.style.use('dark_background')
plt.figure(figsize=(10,2))
sns.displot(netflix_['duration2'])

<seaborn.axisgrid.FacetGrid at 0x2482a8a59d0>

<Figure size 1000x200 with 0 Axes>


bins = [-1,1,50,80,100,120,150,200,315]
labels = ['<1','1-50','50-80','80-100','100-120','120-150','150-200','200-315']
netflix_['duration2'] = pd.cut(netflix_['duration2'],bins = bins, labels = labels )
netflix_.head()


netflix_.loc[~netflix_['duration'].str.contains('Season'),'duration'] = netflix_.loc[~netflix_['duration'].str.contains('Season'),'duration2']
netflix_.drop(['duration2'],axis=1,inplace=True)
netflix_.head()


from datetime import datetime
from dateutil.parser import parse

netflix_["year_added"] = netflix_['date_added'].dt.year
netflix_["year_added"] = netflix_["year_added"].astype("Int64")
netflix_["month_added"] = netflix_['date_added'].dt.month
netflix_['month_name'] = netflix['date_added'].dt.month_name()
netflix_["month_added"] = netflix_["month_added"].astype("Int64")
netflix_["day_added"] = netflix_['date_added'].dt.day
netflix_["day_added"] = netflix_["day_added"].astype("Int64")
netflix_['Weekday_added'] = netflix_['date_added'].apply(lambda x: parse(str(x)).strftime("%A"))
netflix_.head()


netflix_['title'] = netflix_['title'].str.replace(r"\(.*\)","")
netflix_.head()

C:\Users\modem\AppData\Local\Temp\ipykernel_38720\2120036043.py:1: FutureWarning: The default value of regex will change from True to False in a future version.
  netflix_['title'] = netflix_['title'].str.replace(r"\(.*\)","")


netflix_genre=netflix_.groupby(['Genre']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
plt.figure(figsize=(15,6))
sns.barplot(x = "Genre",y = 'title', data = netflix_genre)
plt.xticks(rotation = 60)
plt.title('Top 15 Genres')
plt.show()


netflix_pie = netflix_.groupby(['type']).agg({'title':'nunique'}).reset_index()


netflix_pie


colors = sns.color_palette('bright')[0:5]
plt.figure(figsize=(10,4))

plt.pie(netflix_pie['title'], labels = netflix_pie['type'], colors = colors, autopct='%.0f%%')
plt.title('Percentage of movies and TV shows')
plt.show()


netflix_['Country'] = netflix_['Country'].str.replace(',', '')
netflix_.head()


netflix_country = netflix_.groupby(['Country']).agg({'title':'nunique'}).reset_index().sort_values(by=['title'],ascending=False)[:10]
plt.figure(figsize=(15,6))
sns.barplot(y = "Country",x = 'title', data = netflix_country)
plt.xticks(rotation = 60)
plt.title('Top 10 Countries for content creation')
plt.show()


netflix_rating = netflix_.groupby(['rating']).agg({'title':'nunique'}).reset_index().sort_values(by=['title'],ascending=False)[:10]

plt.figure(figsize=(15,6))
sns.barplot(y = "rating",x = 'title', data = netflix_rating)
plt.xticks(rotation = 60)
plt.title('Top 10 rating types')
plt.show()


netflix_duration = netflix_.groupby(['duration']).agg({'title':'nunique'}).reset_index().sort_values(by=['title'],ascending=False)[:10]

plt.figure(figsize=(15,6))
sns.barplot(y = "duration",x = 'title', data = netflix_duration)
plt.xticks(rotation = 60)
plt.title('Top 10 duaration categories')
plt.show()


netflix_actors = netflix_.groupby(['Actors']).agg({'title':'nunique'}).reset_index().sort_values(by=['title'],ascending=False)[:15]
netflix_actors = netflix_actors[netflix_actors['Actors']!='Unknown Actor']
plt.figure(figsize=(15,6))
sns.barplot(y = "Actors",x = 'title', data = netflix_actors )
plt.xticks(rotation = 60)
plt.title('Top 15 most popular Actors')
plt.show()


netflix_directors = netflix_.groupby(['Directors']).agg({'title':'nunique'}).reset_index().sort_values(by=['title'],ascending=False)[:15]
netflix_directors = netflix_directors[netflix_directors['Directors']!='Unknown Director']
plt.figure(figsize=(15,6))
sns.barplot(y = "Directors",x = 'title', data = netflix_directors )
plt.xticks(rotation = 60)
plt.title('Top 15 most popular Directors')
plt.show

<function matplotlib.pyplot.show(close=None, block=None)>


netflix_.head()


netflix_year = netflix_.groupby(['year_added']).agg({'title':'nunique'}).reset_index()

plt.figure(figsize=(15,6))
sns.lineplot(x = "year_added",y = 'title', data = netflix_year, color = 'red' )
plt.xticks(rotation = 60)
plt.title('movies/ TV shows across the years')
plt.show

<function matplotlib.pyplot.show(close=None, block=None)>


fig = plt.figure(figsize = (15,5))

#plt.style.use('dark_background')
sns.countplot(data = netflix_,x = 'year_added',hue = 'type',palette ="Reds_r")
plt.title('Movies and TV Shows added added to Netflix by date ', fontsize=14)

Text(0.5, 1.0, 'Movies and TV Shows added added to Netflix by date ')


netflix_month = netflix_.groupby(['month_name', 'type']).agg({'title':'nunique'}).reset_index()

plt.figure(figsize=(15,6))
sns.lineplot(x = "month_name",y = 'title', data = netflix_month, color = 'red', hue = netflix_month.type )
plt.xticks(rotation = 60)
plt.title('movies/ TV shows added across the months')
plt.show

<function matplotlib.pyplot.show(close=None, block=None)>


netflix_month = netflix_.groupby(['month_name']).agg({'title':'nunique'}).reset_index()

plt.figure(figsize=(15,6))
sns.lineplot(x = "month_name",y = 'title', data = netflix_month, color = 'red' )
plt.xticks(rotation = 60)
plt.title('movies/ TV shows added across the months')
plt.show

<function matplotlib.pyplot.show(close=None, block=None)>


netflix_day = netflix_.groupby(['day_added']).agg({'title':'nunique'}).reset_index()

plt.figure(figsize=(15,6))
sns.barplot(x = "day_added",y = 'title', data = netflix_day, color = 'red' )
plt.xticks(rotation = 60)
plt.title('movies/ TV shows added across each day')
plt.show

<function matplotlib.pyplot.show(close=None, block=None)>


netflix_weekday = netflix_.groupby(['Weekday_added', 'type']).agg({'title':'nunique'}).reset_index()

plt.figure(figsize=(15,6))
sns.lineplot(x = "Weekday_added",y = 'title', data = netflix_weekday, color = 'red' , hue = netflix_weekday.type)
plt.xticks(rotation = 60)
plt.title('movies/ TV shows added across weekdays')
plt.show

<function matplotlib.pyplot.show(close=None, block=None)>


netflix_weekday = netflix_.groupby(['Weekday_added']).agg({'title':'nunique'}).reset_index()

plt.figure(figsize=(15,6))
sns.lineplot(x = "Weekday_added",y = 'title', data = netflix_weekday, color = 'red' )
plt.xticks(rotation = 60)
plt.title('movies/ TV shows added across weekdays')
plt.show

<function matplotlib.pyplot.show(close=None, block=None)>


plt.figure(figsize=(15,6))
sns.boxplot(x='type', y='release_year', data=netflix_, )
sns.despine(left=True)
plt.title('Type of Show by Release Date')
plt.ylim(2000,2020)

(2000.0, 2020.0)


month_order = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 
               'October', 'November', 'December']
content = netflix_.groupby('year_added')['month_name'].value_counts().unstack().fillna(0)[month_order].T

plt.figure(figsize=(10,8))
plt.title("Number of months' content added per year")
sns.heatmap(content , cmap = 'Blues')
plt.show()


plt.figure(figsize = (12,5))
sns.scatterplot(y = netflix_.index , x = netflix_.release_year , hue = netflix_.type , palette='Set2')

<AxesSubplot:xlabel='release_year'>


netflix_.groupby(['day_added']).agg({"title":"nunique"})


netflix_shows = netflix_[netflix_['type']=='TV Show']
netflix_movies = netflix_[netflix_['type']=='Movie']


netflix_genre = netflix_shows.groupby(['Genre']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
plt.figure(figsize = (15,6))
sns.barplot(y = "Genre",x = 'title', data = netflix_genre)
plt.xticks(rotation = 60)
plt.title('Top 15 Genres')
plt.show()


netflix_genre = netflix_movies.groupby(['Genre']).agg({"title":"nunique"}).reset_index().sort_values(by=['title'],ascending=False)[:15]
plt.figure(figsize = (15,6))
sns.barplot(y = "Genre",x = 'title', data = netflix_genre)
plt.xticks(rotation = 60)
plt.title('Top 15 Genres')
plt.show()


netflix_country = netflix_shows.groupby(['Country']).agg({'title':'nunique'}).reset_index().sort_values(by=['title'],ascending=False)[:10]
plt.figure(figsize=(15,6))
sns.barplot(y = "Country",x = 'title', data = netflix_country)
plt.xticks(rotation = 60)
plt.title('Top 10 Countries for content creation')
plt.show()


netflix_country = netflix_movies.groupby(['Country']).agg({'title':'nunique'}).reset_index().sort_values(by=['title'],ascending=False)[:10]
plt.figure(figsize=(15,6))
sns.barplot(y = "Country",x = 'title', data = netflix_country)
plt.xticks(rotation = 60)
plt.title('Top 10 Countries for content creation')
plt.show()

	Unnamed: 0	show_id	type	title	director	cast	country	date_added	release_year	rating	duration	listed_in	description
0	0	s1	Movie	Dick Johnson Is Dead	Kirsten Johnson	NaN	United States	25-Sep-21	2020	PG-13	90 min	Documentaries	As her father nears the end of his life, filmm...
1	1	s2	TV Show	Blood & Water	NaN	Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban...	South Africa	24-Sep-21	2021	TV-MA	2 Seasons	International TV Shows, TV Dramas, TV Mysteries	After crossing paths at a party, a Cape Town t...
2	2	s3	TV Show	Ganglands	Julien Leclercq	Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi...	NaN	24-Sep-21	2021	TV-MA	1 Season	Crime TV Shows, International TV Shows, TV Act...	To protect his family from a powerful drug lor...
3	3	s4	TV Show	Jailbirds New Orleans	NaN	NaN	NaN	24-Sep-21	2021	TV-MA	1 Season	Docuseries, Reality TV	Feuds, flirtations and toilet talk go down amo...
4	4	s5	TV Show	Kota Factory	NaN	Mayur More, Jitendra Kumar, Ranjan Raj, Alam K...	India	24-Sep-21	2021	TV-MA	2 Seasons	International TV Shows, Romantic TV Shows, TV ...	In a city of coaching centers known to train I...
...	...	...	...	...	...	...	...	...	...	...	...	...	...
8802	8802	s8803	Movie	Zodiac	David Fincher	Mark Ruffalo, Jake Gyllenhaal, Robert Downey J...	United States	20-Nov-19	2007	R	158 min	Cult Movies, Dramas, Thrillers	A political cartoonist, a crime reporter and a...
8803	8803	s8804	TV Show	Zombie Dumb	NaN	NaN	NaN	1-Jul-19	2018	TV-Y7	2 Seasons	Kids' TV, Korean TV Shows, TV Comedies	While living alone in a spooky town, a young g...
8804	8804	s8805	Movie	Zombieland	Ruben Fleischer	Jesse Eisenberg, Woody Harrelson, Emma Stone, ...	United States	1-Nov-19	2009	R	88 min	Comedies, Horror Movies	Looking to survive in a world taken over by zo...
8805	8805	s8806	Movie	Zoom	Peter Hewitt	Tim Allen, Courteney Cox, Chevy Chase, Kate Ma...	United States	11-Jan-20	2006	PG	88 min	Children & Family Movies, Comedies	Dragged from civilian life, a former superhero...
8806	8806	s8807	Movie	Zubaan	Mozez Singh	Vicky Kaushal, Sarah-Jane Dias, Raaghav Chanan...	India	2-Mar-19	2015	TV-14	111 min	Dramas, International Movies, Music & Musicals	A scrappy but poor boy worms his way into a ty...

	Unnamed: 0	show_id	type	title	director	cast	country	date_added	release_year	rating	duration	listed_in	description
0	0	s1	Movie	Dick Johnson Is Dead	Kirsten Johnson	NaN	United States	25-Sep-21	2020	PG-13	90 min	Documentaries	As her father nears the end of his life, filmm...
1	1	s2	TV Show	Blood & Water	NaN	Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban...	South Africa	24-Sep-21	2021	TV-MA	2 Seasons	International TV Shows, TV Dramas, TV Mysteries	After crossing paths at a party, a Cape Town t...
2	2	s3	TV Show	Ganglands	Julien Leclercq	Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi...	NaN	24-Sep-21	2021	TV-MA	1 Season	Crime TV Shows, International TV Shows, TV Act...	To protect his family from a powerful drug lor...
3	3	s4	TV Show	Jailbirds New Orleans	NaN	NaN	NaN	24-Sep-21	2021	TV-MA	1 Season	Docuseries, Reality TV	Feuds, flirtations and toilet talk go down amo...
4	4	s5	TV Show	Kota Factory	NaN	Mayur More, Jitendra Kumar, Ranjan Raj, Alam K...	India	24-Sep-21	2021	TV-MA	2 Seasons	International TV Shows, Romantic TV Shows, TV ...	In a city of coaching centers known to train I...

	Unnamed: 0	release_year
count	8807.000000	8807.000000
mean	4403.000000	2014.180198
std	2542.506244	8.819312
min	0.000000	1925.000000
25%	2201.500000	2013.000000
50%	4403.000000	2017.000000
75%	6604.500000	2019.000000
max	8806.000000	2021.000000

	Unnamed: 0	show_id	type	title	director	cast	country	date_added	release_year	rating	duration	listed_in	description
count	8807.000000	8807	8807	8807	6173	7982	7976	8797	8807.000000	8803	8804	8807	8807
unique	NaN	8807	2	8804	4528	7692	746	1767	NaN	17	220	514	8775
top	NaN	s1	Movie	15-Aug	Rajiv Chilaka	David Attenborough	United States	1-Jan-20	NaN	TV-MA	1 Season	Dramas, International Movies	Paranormal activity at a lush, abandoned prope...
freq	NaN	1	6131	2	19	19	2818	109	NaN	3207	1793	362	4
mean	4403.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2014.180198	NaN	NaN	NaN	NaN
std	2542.506244	NaN	NaN	NaN	NaN	NaN	NaN	NaN	8.819312	NaN	NaN	NaN	NaN
min	0.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1925.000000	NaN	NaN	NaN	NaN
25%	2201.500000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2013.000000	NaN	NaN	NaN	NaN
50%	4403.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2017.000000	NaN	NaN	NaN	NaN
75%	6604.500000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2019.000000	NaN	NaN	NaN	NaN
max	8806.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2021.000000	NaN	NaN	NaN	NaN

	Unnamed: 0	show_id	type	title	director	cast	country	date_added	release_year	rating	duration	listed_in	description
693	693	s694	Movie	Ali & Ratu Ratu Queens	Lucky Kuswandi	Iqbaal Ramadhan, Nirina Zubir, Asri Welas, Tik...	NaN	17-Jun-21	2021	TV-14	101 min	Comedies, Dramas, International Movies	After his father's passing, a teenager sets ou...
781	781	s782	Movie	Black Holes \| The Edge of All We Know	Peter Galison	NaN	NaN	2-Jun-21	2021	TV-14	99 min	Documentaries	Follow scientists on their quest to understand...
762	762	s763	Movie	Sweet & Sour	Lee Kae-byeok	Jang Ki-yong, Chae Soo-bin, Jung Soo-jung	South Korea	4-Jun-21	2021	TV-14	103 min	Comedies, International Movies, Romantic Movies	Faced with real-world opportunities and challe...
763	763	s764	TV Show	Sweet Tooth	NaN	Nonso Anozie, Christian Convery, Adeel Akhtar,...	United States	4-Jun-21	2021	TV-14	1 Season	TV Action & Adventure, TV Dramas, TV Sci-Fi & ...	On a perilous adventure across a post-apocalyp...
764	764	s765	Movie	Trippin' with the Kandasamys	Jayan Moodley	Jailoshini Naidoo, Maeshni Naicker, Madhushan ...	South Africa	4-Jun-21	2021	TV-14	94 min	Comedies, International Movies, Romantic Movies	To rekindle their marriages, best friends-turn...

Table of Contents¶

1. Problem Statement¶

1.1. Introduction¶

About Data¶

This dataset contains data collected from Netflix of different TV shows and movies from the year 2008 to 2021.¶

1.2. Data source and dataset¶

2. Import Packages¶

Loading Dataset¶

3. Data Profiling¶

Post Profiling¶

EDA¶

Bivariate Analysis¶

Univariate Analysis separately for shows and movies¶

4. Conclusion¶

Business Insights¶

Recommendations¶

	title	Actors
0	Dick Johnson Is Dead	nan
1	Blood & Water	Ama Qamata
2	Blood & Water	Khosi Ngema
3	Blood & Water	Gail Mabalane
4	Blood & Water	Thabang Molaba
5	Blood & Water	Dillon Windvogel
6	Blood & Water	Natasha Thahane
7	Blood & Water	Arno Greeff
8	Blood & Water	Xolile Tshabalala
9	Blood & Water	Getmore Sithole

	Total Missing	In Percent
date_added	158	0.08
rating	67	0.03
duration	3	0.00
title	0	0.00
Actors	0	0.00
Directors	0	0.00
Genre	0	0.00
Country	0	0.00
show_id	0	0.00
type	0	0.00

	title	Actors	Directors	Genre	Country	show_id	type	date_added	release_year	rating	duration
0	Dick Johnson Is Dead	Unknown Actor	Kirsten Johnson	Documentaries	United States	s1	Movie	25-Sep-21	2020	PG-13	90 min
1	Blood & Water	Ama Qamata	Unknown Director	International TV Shows	South Africa	s2	TV Show	24-Sep-21	2021	TV-MA	2 Seasons
2	Blood & Water	Ama Qamata	Unknown Director	TV Dramas	South Africa	s2	TV Show	24-Sep-21	2021	TV-MA	2 Seasons
3	Blood & Water	Ama Qamata	Unknown Director	TV Mysteries	South Africa	s2	TV Show	24-Sep-21	2021	TV-MA	2 Seasons
4	Blood & Water	Khosi Ngema	Unknown Director	International TV Shows	South Africa	s2	TV Show	24-Sep-21	2021	TV-MA	2 Seasons

	Total Missing	In Percent
Country	12497	6.11
date_added	158	0.08
rating	67	0.03
duration	3	0.00
title	0	0.00
Actors	0	0.00
Directors	0	0.00
Genre	0	0.00
show_id	0	0.00
type	0	0.00

	title	Actors	Directors	Genre	Country	show_id	type	date_added	release_year	rating	duration
129171	Louis C.K. 2017	Louis C.K.	Louis C.K.	Movies	United States	s5542	Movie	4-Apr-17	2017	74 min	NaN
134237	Louis C.K.: Hilarious	Louis C.K.	Louis C.K.	Movies	United States	s5795	Movie	16-Sep-16	2010	84 min	NaN
134371	Louis C.K.: Live at the Comedy Store	Louis C.K.	Louis C.K.	Movies	United States	s5814	Movie	15-Aug-16	2015	66 min	NaN

	title
day_added
1	2219
2	325
3	151
4	175
5	231
6	210
7	190
8	201
9	148
10	214
11	149
12	181
13	175
14	198
15	688
16	289
17	180
18	205
19	243
20	248
21	190
22	230
23	182
24	159
25	196
26	205
27	195
28	190
29	140
30	210
31	274

	Unnamed: 0	release_year
Unnamed: 0	1.000000	-0.246713
release_year	-0.246713	1.000000